Intra- and inter-patient tissue variability are seldom implemented in digital phantoms for imaging simulations, which can lead to issues when developing and evaluating material differentiation methods. In this work, we evaluated two methods for generating variability in tissue attenuation properties based on measured properties of human tissue. Our goal is to find a sampling method that generates attenuation curves within measured distributions. The first approach parameterizes tissue attenuation curves as a linear combination of aluminum and PMMA. The second approach is based on the Midgley decomposition model, where the attenuation curve is expressed in terms of five coefficients. Attenuation curves were generated by sampling the two- and five-parameter spaces, and they were compared to previous measurements in ex-vivo adipose tissue acquired at 8 , 11, 15, 20 and 30keV. The average differences of the sampled curves relative to the measurements were 1.68% (2-parameter) and 1.31% (5-parameter), and the absolute differences in coefficients of variation were under 2% for both methods. These results indicate that both methods captured the variability present in measured attenuation curves. This study provides preliminary insights into the effectiveness of two methods for adding tissue variability to imaging simulations.
Despite the widespread use of cascaded linear models in x-ray imaging, no publicly available implementation exists as of now. Our goal was to implement the cascaded linear systems theory and create a flexible and publicly available code capable of modeling direct and indirect-conversion x-ray imaging detectors under different acquisition conditions.
Contrast-enhanced digital mammography (CEDM) is used to detect iodine uptake in breast lesions. Iodine concentrations inside or around breast lesions could be used as a biomarker, provided a properly characterized quantification method is implemented. In this work, we have evaluated a method to quantify iodine concentrations in CEDM in terms of its intrinsic linearity, bias and variability. This evaluation was performed in a virtual clinical trial (VCT) environment, simulating anthropomorphic breast phantoms containing solid and liquid lesions with different iodine concentrations. Our results showed that anatomical variables such as breast size and lesion size and composition have a considerable effect on the iodine quantification. The method was linear in the clinical iodine concentration range, and showed an approximately constant 1 mg/cm2 bias in the 0 – 2 mg/cm2 range for both solid and liquid lesions. Corrections were proposed that reduced the variability due to breast size, lesion size, and composition.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.